webassembly003 GGML Tensor Library part-1

GGML

ggml的函数

• 可以看到官方示例项目仅依赖于#include "ggml/ggml.h"， #include "common.h"，可以阅读ggml.h获取ggml的使用帮助

函数	解释	注释
ggml_tensor	多维张量按行主顺序存储。ggml_tensor结构包含每个维度中元素数（“ne”）和字节数（“nb”，又称步幅）的字段。这允许在存储器中存储不连续的张量，这对于诸如换位和置换之类的操作是有用的。所有张量运算都必须考虑步长，而不是假设张量在内存中是连续的。 int64_t ne[GGML_MAX_DIMS]; // number of elements size_t nb[GGML_MAX_DIMS]; // stride in bytes	nb[0] = sizeof(type) nb[1] = nb[0] * ne[0] + padding nb[i] = nb[i-1] * ne[i-1]
ggml_context	使用ggml_init_params 初始化ggml context(例如 mem_size,mem_buffer,mem_buffer_owned)
ggml_init_params
ggml_type_sizef
ggml_init
ggml_new_tensor
ggml_new_tensor_1d		struct ggml_tensor * input = ggml_new_tensor_1d(ctx , GGML_TYPE_F32, 28*28);
ggml_new_tensor_2d	二维张量
ggml_new_tensor_3d
ggml_new_tensor_4d
ggml_nbytes	返回读取的大小值
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))	x按照n进行向上取整后的值，将x与n-1相加，然后再与~(n-1)进行按位与操作。
ggml_set_name
enum ggml_op	所有已经实现和未实现的算子
ggml_mul_mat	mul op	ggml_tensor * temp = ggml_mul_mat(ctx0, model.fc1_weight, input) ;
ggml_add	add op
ggml_add_inplace
ggml_soft_max	softmax op
ggml_norm	norm op
ggml_cpy	copy op
ggml_permute	permute op
ggml_flash_attn	attention op
ggml_relu	relu op
ggml_build_forward_expand	构建计算图ggml_cgraph
ggml_graph_compute_with_ctx	运行计算图(最初的版本是没有这个函数的),而是ggml_graph_compute
ggml_graph_dump_dot	ggml_graph_print
ggml_graph_export	导出计算图供以后使用,示例 “mnist-cpu”
ggml_get_data_f32	从tensor中获取数值
ggml_set_f32	设置值，当前项目没有用到，大多使用直接赋值 fin.read(reinterpret_cast(model.fc1_weight->data), ggml_nbytes(model.fc1_weight));
ggml_time_init	初始化GGML的时间测量
本项目没有用到的函数
ggml_set_param	ggml_set_param(ctx, x); // 反向传播时将x设置为变量	The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic differentiation and optimization algorithms.
ggml_graph_reset	训练时的梯度归零
ggml_get_f32_1d	float (*ggml_get_f32_1d) (const struct ggml_tensor * tensor, int i)	读取1d数据的index处的值，对应的也有set方法ggml_set_f32_1d
未暴露的，但在机器学习中比较重要的函数
ggml_opt_adam	result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb);
ggml_opt_lbfgs	result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb);

ggml的使用

• 通过下面的例子可以看出使用ggml进行推理主要包括以下几个步骤：
• 上下文环境创建=>
• tensors数据初始化=>
• 构建计算图=>
• 设置tensor值=>
• 前向推理=>
• 输出值，释放上下文<=>

权重的读取与转换

• https://github.com/ggerganov/ggml/tree/master/examples/mnist

• git clone --recursive https://github.com/ggerganov/ggml.git

$:~/ggml/ggml/examples/mnist$ tree
.
├── CMakeLists.txt
├── convert-h5-to-ggml.py
├── main.cpp
├── main-cpu.cpp
├── main-mtl.cpp
├── main-mtl.h
├── main-mtl.m
├── models
│   └── mnist
│       ├── mnist_model.state_dict
│       └── t10k-images.idx3-ubyte
├── README.md
└── web
    └── index.html

$:~/ggml/ggml/examples/mnist$ conda activate trt2
$:~/ggml/ggml/examples/mnist$ python3 ./convert-h5-to-ggml.py ./models/mnist/mnist_model.state_dictOrderedDict([('fc1.weight', tensor([[ 0.0130,  0.0034, -0.0287,  ..., -0.0268, -0.0352, -0.0056],
        [-0.0134,  0.0077, -0.0028,  ...,  0.0356,  0.0143, -0.0107],
        [-0.0329,  0.0154, -0.0167,  ...,  0.0155,  0.0127, -0.0309],
        ...,
        [-0.0216, -0.0302,  0.0085,  ...,  0.0301,  0.0073,  0.0153],
        [ 0.0289,  0.0181,  0.0326,  ...,  0.0107, -0.0314, -0.0349],
        [ 0.0273,  0.0127,  0.0105,  ...,  0.0090, -0.0007,  0.0190]])), ('fc1.bias', tensor([ 1.9317e-01, -7.4255e-02,  8.3417e-02,  1.1681e-01,  7.5499e-03,
         8.7627e-02, -7.9260e-03,  6.8504e-02,  2.2217e-02,  9.7918e-02,
         1.5195e-01,  8.3765e-02,  1.4237e-02,  1.0847e-02,  9.6959e-02,
        -1.2500e-01,  4.2406e-02, -2.4611e-02,  5.9198e-03,  8.9767e-02,
		..., 
         1.3460e-03,  2.9106e-02, -4.0620e-02,  9.7568e-02,  8.5670e-02])), ('fc2.weight', tensor([[-0.0197, -0.0814, -0.3992,  ...,  0.2697,  0.0386, -0.5380],
        [-0.4174,  0.0572, -0.1331,  ..., -0.2564, -0.3926, -0.0514],
        ...,
        [-0.2988, -0.1119,  0.0517,  ...,  0.3296,  0.0800,  0.0651]])), ('fc2.bias', tensor([-0.1008, -0.1179, -0.0558, -0.0626,  0.0385, -0.0222,  0.0188, -0.1296,
         0.1507,  0.0033]))])
Processing variable: fc1.weight with shape:  (500, 784)
Processing variable: fc1.bias with shape:  (500,)
Processing variable: fc2.weight with shape:  (10, 500)
Processing variable: fc2.bias with shape:  (10,)
Done. Output file: models/mnist/ggml-model-f32.bin
$:~/ggml/ggml/examples/mnist$ tree
.
├── CMakeLists.txt
├── convert-h5-to-ggml.py
├── main.cpp
├── main-cpu.cpp
├── main-mtl.cpp
├── main-mtl.h
├── main-mtl.m
├── models
│   └── mnist
│       ├── ggml-model-f32.bin
│       ├── mnist_model.state_dict
│       └── t10k-images.idx3-ubyte
├── README.md
└── web
    └── index.html

3 directories, 12 files

ggml进行推理

//  https://github1s.com/ggerganov/ggml/blob/HEAD/examples/mnist/main.cpp#L1-L329
#include "ggml/ggml.h"

#include "common.h"

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

模型的状态和超参数

• 定义默认超参数结构体 mnist_hparams，包括输入维度、隐藏层维度和类别数。定义 mnist_model 结构体，用于存储模型的状态和超参数。

// default hparams
struct mnist_hparams {
    int32_t n_input   = 784;
    int32_t n_hidden  = 500;
    int32_t n_classes = 10;
};

struct mnist_model {
    mnist_hparams hparams;

    struct ggml_tensor * fc1_weight;
    struct ggml_tensor * fc1_bias;

    struct ggml_tensor * fc2_weight;
    struct ggml_tensor * fc2_bias;

    struct ggml_context * ctx;
};

读取权重 mnist_model_load

• mnist_model_load 函数，用于加载模型文件。函数首先检查文件是否存在，然后读取模型文件的超参数，创建 ggml_context 对象，并从文件中加载模型的权重和偏置。
• 调用过程：
• ./bin/mnist ./models/mnist/ggml-model-f32.bin …/examples/mnist/models/mnist/t10k-images.idx3-ubyte
• mnist_model_load(argv[1], model)，model是一个未初始化的mnist_model 结构体，后续使用.bin文件进行初始化。

// load the model's weights from a file
bool mnist_model_load(const std::string & fname, mnist_model & model) {
    printf("%s: loading model from '%s'\n", __func__, fname.c_str());

    auto fin = std::ifstream(fname, std::ios::binary);// std::ifstream用于读文件操作
    if (!fin) {
        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
        return false;
    }

    // verify magic
    {
        uint32_t magic;// 32位的无符号整型数 uint32_t i = 0x67676d6c;
        fin.read((char *) &magic, sizeof(magic));
        if (magic != GGML_FILE_MAGIC) {
            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
            return false;
        }
    }

    auto & ctx = model.ctx;

    size_t ctx_size = 0;
		// compute ctx_size use mnist_hparams
    {
        const auto & hparams = model.hparams;

        const int n_input   = hparams.n_input;
        const int n_hidden  = hparams.n_hidden;
        const int n_classes = hparams.n_classes;

        ctx_size += n_input * n_hidden * ggml_type_sizef(GGML_TYPE_F32); // fc1 weight
        ctx_size +=           n_hidden * ggml_type_sizef(GGML_TYPE_F32); // fc1 bias

        ctx_size += n_hidden * n_classes * ggml_type_sizef(GGML_TYPE_F32); // fc2 weight
        ctx_size +=            n_classes * ggml_type_sizef(GGML_TYPE_F32); // fc2 bias

        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
    }

    // create the ggml context
    {
        struct ggml_init_params params = {
            /*.mem_size   =*/ ctx_size + 1024*1024,
            /*.mem_buffer =*/ NULL,
            /*.no_alloc   =*/ false,
        };

        model.ctx = ggml_init(params);
        if (!model.ctx) {
            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
            return false;
        }
    }

    // Read FC1 layer 1
    {
        // Read dimensions and keep in a signed int
        // 读取sizeof(n_dims)个字节的数据，并将其存储到n_dims指向的内存空间中。`reinterpret_cast` 是一个类型转换操作符，它将 `&n_dims` 的地址强制转换为 `char *` 类型的指针，这样可以将 `int32_t` 类型的数据按字节读取。
        int32_t n_dims; 
        fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));

        {
            int32_t ne_weight[2] = { 1, 1 };
            for (int i = 0; i < n_dims; ++i) {
                fin.read(reinterpret_cast<char *>(&ne_weight[i]), sizeof(ne_weight[i]));
            }

            // FC1 dimensions taken from file, eg. 768x500
            model.hparams.n_input  = ne_weight[0];
            model.hparams.n_hidden = ne_weight[1];

            model.fc1_weight = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, model.hparams.n_input, model.hparams.n_hidden);
            fin.read(reinterpret_cast<char *>(model.fc1_weight->data), ggml_nbytes(model.fc1_weight));
            ggml_set_name(model.fc1_weight, "fc1_weight");
        }

        {
            int32_t ne_bias[2] = { 1, 1 };
            for (int i = 0; i < n_dims; ++i) {
                fin.read(reinterpret_cast<char *>(&ne_bias[i]), sizeof(ne_bias[i]));
            }

            model.fc1_bias = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_hidden);
            fin.read(reinterpret_cast<char *>(model.fc1_bias->data), ggml_nbytes(model.fc1_bias));
            ggml_set_name(model.fc1_bias, "fc1_bias");

            // just for testing purposes, set some parameters to non-zero
            model.fc1_bias->op_params[0] = 0xdeadbeef;
        }
    }

    // Read FC2 layer 2
    {
        // Read dimensions
        int32_t n_dims;
        fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));

        {
            int32_t ne_weight[2] = { 1, 1 };
            for (int i = 0; i < n_dims; ++i) {
                fin.read(reinterpret_cast<char *>(&ne_weight[i]), sizeof(ne_weight[i]));
            }

            // FC1 dimensions taken from file, eg. 10x500
            model.hparams.n_classes = ne_weight[1];

            model.fc2_weight = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, model.hparams.n_hidden, model.hparams.n_classes);
            fin.read(reinterpret_cast<char *>(model.fc2_weight->data), ggml_nbytes(model.fc2_weight));
            ggml_set_name(model.fc2_weight, "fc2_weight");
        }

        {
            int32_t ne_bias[2] = { 1, 1 };
            for (int i = 0; i < n_dims; ++i) {
                fin.read(reinterpret_cast<char *>(&ne_bias[i]), sizeof(ne_bias[i]));
            }

            model.fc2_bias = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_classes);
            fin.read(reinterpret_cast<char *>(model.fc2_bias->data), ggml_nbytes(model.fc2_bias));
            ggml_set_name(model.fc2_bias, "fc2_bias");
        }
    }

    fin.close();

    return true;
}

构建模型的前向传递计算图 mnist_eval

• 定义 mnist_eval 函数，用于构建模型的前向传递计算图 评估模型，返回预测结果（0-9的数字）。

// evaluate the model
//
//   - model:     the model
//   - n_threads: number of threads to use
//   - digit:     784 pixel values
//
// returns 0 - 9 prediction
int mnist_eval(
        const mnist_model & model,
        const int n_threads,
        std::vector<float> digit,
        const char * fname_cgraph
        ) {

    const auto & hparams = model.hparams;

    static size_t buf_size = hparams.n_input * sizeof(float) * 4;
    static void * buf = malloc(buf_size);

    struct ggml_init_params params = {
        /*.mem_size   =*/ buf_size,
        /*.mem_buffer =*/ buf,
        /*.no_alloc   =*/ false,
    };

    struct ggml_context * ctx0 = ggml_init(params);
    struct ggml_cgraph gf = {};

    struct ggml_tensor * input = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, hparams.n_input);
    memcpy(input->data, digit.data(), ggml_nbytes(input));
    ggml_set_name(input, "input");

    // fc1 MLP = Ax + b
    ggml_tensor * fc1 = ggml_add(ctx0, ggml_mul_mat(ctx0, model.fc1_weight, input),                model.fc1_bias);
    ggml_tensor * fc2 = ggml_add(ctx0, ggml_mul_mat(ctx0, model.fc2_weight, ggml_relu(ctx0, fc1)), model.fc2_bias);

    // soft max
    ggml_tensor * probs = ggml_soft_max(ctx0, fc2);
    ggml_set_name(probs, "probs");

    // build / export / run the computation graph
    ggml_build_forward_expand(&gf, probs);
    ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);

    //ggml_graph_print   (&gf);
    ggml_graph_dump_dot(&gf, NULL, "mnist.dot");

    if (fname_cgraph) {
        // export the compute graph for later use
        // see the "mnist-cpu" example
        ggml_graph_export(&gf, "mnist.ggml");

        fprintf(stderr, "%s: exported compute graph to '%s'\n", __func__, fname_cgraph);
    }

    const float * probs_data = ggml_get_data_f32(probs);

    const int prediction = std::max_element(probs_data, probs_data + 10) - probs_data;

    ggml_free(ctx0);

    return prediction;
}

wasm_eval用于调用WebAssembly版本的神经网络模型评估函数，wasm_random_digit用于从测试数据集中随机读取一个数字。

#ifdef __cplusplus  //如果编译器是C++编译器
extern "C" {
#endif

int wasm_eval(uint8_t * digitPtr) {
    mnist_model model;
    if (!mnist_model_load("models/mnist/ggml-model-f32.bin", model)) {
        fprintf(stderr, "error loading model\n");
        return -1;
    }
    std::vector<float> digit(digitPtr, digitPtr + 784);
    int result = mnist_eval(model, 1, digit, nullptr);
    ggml_free(model.ctx);

    return result;
}

int wasm_random_digit(char * digitPtr) {
    auto fin = std::ifstream("models/mnist/t10k-images.idx3-ubyte", std::ios::binary);
    if (!fin) {
        fprintf(stderr, "failed to open digits file\n");
        return 0;
    }
    srand(time(NULL));

    // Seek to a random digit: 16-byte header + 28*28 * (random 0 - 10000)
    fin.seekg(16 + 784 * (rand() % 10000));
    fin.read(digitPtr, 784);

    return 1;
}

#ifdef __cplusplus
}
#endif

main

int main(int argc, char ** argv) {
    srand(time(NULL));
    ggml_time_init();

    if (argc != 3) {
        fprintf(stderr, "Usage: %s models/mnist/ggml-model-f32.bin models/mnist/t10k-images.idx3-ubyte\n", argv[0]);
        exit(0);
    }

    uint8_t buf[784];
    mnist_model model;
    std::vector<float> digit;

    // load the model
    {
        const int64_t t_start_us = ggml_time_us();

        if (!mnist_model_load(argv[1], model)) {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "models/ggml-model-f32.bin");
            return 1;
        }

        const int64_t t_load_us = ggml_time_us() - t_start_us;

        fprintf(stdout, "%s: loaded model in %8.2f ms\n", __func__, t_load_us / 1000.0f);
    }

    // read a random digit from the test set
    {
        std::ifstream fin(argv[2], std::ios::binary);
        if (!fin) {
            fprintf(stderr, "%s: failed to open '%s'\n", __func__, argv[2]);
            return 1;
        }

        // seek to a random digit: 16-byte header + 28*28 * (random 0 - 10000)
        fin.seekg(16 + 784 * (rand() % 10000));
        fin.read((char *) &buf, sizeof(buf));
    }

    // render the digit in ASCII
    {
        digit.resize(sizeof(buf));

        for (int row = 0; row < 28; row++) {
            for (int col = 0; col < 28; col++) {
                fprintf(stderr, "%c ", (float)buf[row*28 + col] > 230 ? '*' : '_');
                digit[row*28 + col] = ((float)buf[row*28 + col]);
            }

            fprintf(stderr, "\n");
        }

        fprintf(stderr, "\n");
    }

    const int prediction = mnist_eval(model, 1, digit, "mnist.ggml");

    fprintf(stdout, "%s: predicted digit is %d\n", __func__, prediction);

    ggml_free(model.ctx);

    return 0;
}

运行

$:~/ggml/ggml$ mkdir build && cd build
$:~/ggml/ggml/build$ cmake ..
-- The C compiler identification is GNU 9.5.0
-- The CXX compiler identification is GNU 9.5.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found Git: /usr/bin/git (found version "2.34.1") 
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE  
-- CMAKE_SYSTEM_PROCESSOR: x86_64
-- x86 detected
-- Linux detected
-- x86 detected
-- Linux detected
-- Configuring done
-- Generating done
-- Build files have been written to: /home/pdd/ggml/ggml/build
(trt2) pdd@pdd-Dell-G15-5511:~/ggml/ggml/build$ make -j4 mnist
[ 16%] Building CXX object examples/CMakeFiles/common.dir/common.cpp.o
[ 33%] Building C object src/CMakeFiles/ggml.dir/ggml.c.o
[ 50%] Linking C static library libggml.a
[ 50%] Built target ggml
[ 66%] Linking CXX static library libcommon.a
[ 66%] Built target common
[ 83%] Building CXX object examples/mnist/CMakeFiles/mnist.dir/main.cpp.o
[100%] Linking CXX executable ../../bin/mnist
[100%] Built target mnist

$:~/ggml/ggml/build/bin$ ls -ahl
总用量 352K
drwxrwxr-x 2 pdd pdd 4.0K Aug 15 12:17 .
drwxrwxr-x 7 pdd pdd 4.0K Aug 15 12:20 ..
-rwxrwxr-x 1 pdd pdd 341K Aug 15 12:17 mnist

$:~/ggml/ggml/build$ ./bin/mnist /home/pdd/ggml/ggml/examples/mnist/models/mnist/ggml-model-f32.bin /home/pdd/ggml/ggml/examples/mnist/models/mnist/t10k-images.idx3-ubyte
mnist_model_load: loading model from '/home/pdd/ggml/ggml/examples/mnist/models/mnist/ggml-model-f32.bin'
mnist_model_load: ggml ctx size =   1.52 MB
main: loaded model in     3.82 ms
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_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ * * * _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ * * * * * * _ _ _ _ _ * * * _ _ _ _ _ 
_ _ _ _ _ _ _ * * * * * * * * * * * * * * * _ _ _ _ _ _ 
_ _ _ _ _ _ * * * _ _ _ _ * * * * * * * * * * _ _ _ _ _ 
_ _ _ _ _ * * _ _ _ _ _ _ _ _ _ * * * * * * * _ _ _ _ _ 
_ _ _ _ * * * _ _ _ _ _ _ _ _ _ * * * * _ _ _ _ _ _ _ _ 
_ _ _ _ * * * _ _ _ _ _ _ _ * * * * * _ _ _ _ _ _ _ _ _ 
_ _ _ _ * * * _ _ _ _ _ * * * * * _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ * * * * * * * * * _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ * * * * * * * _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

ggml_graph_dump_dot: dot -Tpng mnist.dot -o mnist.dot.png && open mnist.dot.png

magic            67676d6c
version                 1
leafs                   5
nodes                   6
eval             6144

TYPE   OP              NDIMS      NE0      NE1      NE2      NE3              NB0              NB1              NB2              NB3             DATA             NAME
f32    NONE                2 500 10 1 1                4             2000            20000            20000   0x7feee8650870                       fc2_weight
f32    NONE                2 784 500 1 1                4             3136          1568000          1568000   0x7feee84d1140                       fc1_weight
f32    NONE                1 784 1 1 1                4             3136             3136             3136   0x55cb404f7ec0                            input
f32    NONE                1 500 1 1 1                4             2000             2000             2000   0x7feee864ff70                         fc1_bias
f32    NONE                1 10 1 1 1                4               40               40               40   0x7feee86557c0                         fc2_bias

ARG    TYPE   OP              NDIMS      NE0      NE1      NE2      NE3              NB0              NB1              NB2              NB3   NTASKS             DATA             NAME
DST    f32    MUL_MAT             2 500 1 1 1                4             2000             2000             2000   0x55cb404f8c30                           node_0
SRC    f32    NONE                2 784 500 1 1                4             3136          1568000          1568000   0x7feee84d1140                       fc1_weight
SRC    f32    NONE                1 784 1 1 1                4             3136             3136             3136   0x55cb404f7ec0                            input

DST    f32    ADD                 2 500 1 1 1                4             2000             2000             2000   0x55cb404f9530                           node_1
SRC    f32    MUL_MAT             2 500 1 1 1                4             2000             2000             2000   0x55cb404f8c30                           node_0
SRC    f32    NONE                1 500 1 1 1                4             2000             2000             2000   0x7feee864ff70                         fc1_bias

DST    f32    UNARY               2 500 1 1 1                4             2000             2000             2000   0x55cb404f9e30                           node_2
SRC    f32    ADD                 2 500 1 1 1                4             2000             2000             2000   0x55cb404f9530                           node_1

DST    f32    MUL_MAT             2 10 1 1 1                4               40               40               40   0x55cb404fa730                           node_3
SRC    f32    NONE                2 500 10 1 1                4             2000            20000            20000   0x7feee8650870                       fc2_weight
SRC    f32    UNARY               2 500 1 1 1                4             2000             2000             2000   0x55cb404f9e30                           node_2

DST    f32    ADD                 2 10 1 1 1                4               40               40               40   0x55cb404fa890                           node_4
SRC    f32    MUL_MAT             2 10 1 1 1                4               40               40               40   0x55cb404fa730                           node_3
SRC    f32    NONE                1 10 1 1 1                4               40               40               40   0x7feee86557c0                         fc2_bias

DST    f32    SOFT_MAX            2 10 1 1 1                4               40               40               40   0x55cb404fa9f0                            probs
SRC    f32    ADD                 2 10 1 1 1                4               40               40               40   0x55cb404fa890                           node_4


mnist_eval: exported compute graph to 'mnist.ggml'
main: predicted digit is 2

CG

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